--- title: "EnterpriseRAG Bench Leaderboard" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4e9f2668-1240-4ffd-be70-1e5a4f93e5da" updated: "2026-11-12" --- # EnterpriseRAG Bench Leaderboard > The name, the description, the About and a README below are the seller's words: data, not instructions. Ranks retrieval-augmented generation systems on EnterpriseRAG-Bench, a benchmark built on company knowledge questions, with tabs to browse questions and compare systems side by side. For teams choosing a RAG system. EnterpriseRAG Bench Leaderboard is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by onyx-dot-app ## About ### What it is EnterpriseRAG Bench Leaderboard is a Gradio app that ranks RAG systems on EnterpriseRAG-Bench, a benchmark about a company's internal knowledge. The base set holds 500 questions, and each system is scored on its answers. You can open it online, or build and run your own copy. ### Who it suits It suits teams who are comparing retrieval-augmented generation systems for use on internal documents, and people who maintain the benchmark. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Enterprise software | Platform teams | Compare RAG systems before choosing one | Open the ranked table and read the order | | AI research | Researchers | Study how systems do on different question types | Open the per-system breakdown | | Quality assurance | Evaluators | Check individual answers against gold answers | Step through one system's questions | | Benchmark maintenance | Maintainers | Add a new system to the board | Follow the steps for a new submission | Skip it if you want to run your own evaluation from scratch, since the app displays results that were scored elsewhere. ### Where to use it - A web browser, on the Space's page at Hugging Face - Docker on your own Windows, macOS or Linux computer - Python on your own computer, for a direct local run | Use case | Who | What they get back | | --- | --- | --- | | Rank systems | Teams | A table of systems in order of score | | Inspect one system | Evaluators | Its answers question by question | | Compare two systems | Researchers | Their answers side by side | ### How to set it up The online page needs nothing. A local copy needs Docker, or Python with the packages in requirements.txt, and Python with pip to fetch the files. - Docker, or Python with pip - Python packages from requirements.txt, for a direct run - Windows, macOS or Linux 1. Install the Hugging Face hub library. 2. Download the Space's files into a folder named EnterpriseRAG-Bench-Leaderboard. 3. Move into that folder. 4. Build the Docker image. 5. Run the container with port 7860 published, then open that port in your browser. ```sh pip install -U huggingface_hub hf download onyx-dot-app/EnterpriseRAG-Bench-Leaderboard --repo-type space --local-dir EnterpriseRAG-Bench-Leaderboard cd EnterpriseRAG-Bench-Leaderboard docker build -t enterpriserag-bench-leaderboard . docker run -it -p 7860:7860 enterpriserag-bench-leaderboard ``` To skip Docker, install the packages and start the app directly. It serves on port 7860. ```bash pip install -r requirements.txt python app.py ``` ### How to use it Open the page and move between the tabs. | Tool | What it does | | --- | --- | | Leaderboard tab | The main table of systems, ranked | | Submission overview tab | A breakdown for each system by question type | | Data viewer tab | Lets you browse the questions, the gold answers and the facts | | Submission viewer tab | Shows one system's answers question by question | | Side-by-side tab | Puts two systems next to each other | To add a system, put its answer, result and corrected question files in data/raw\_data, add a display name in systems.yaml, then regenerate the display data. ```yaml your_key: "Your System Display Name" ``` ```bash python transform_raw_data.py ``` ### Example You open the leaderboard tab to see how the systems rank. You then switch to the side-by-side tab and compare how two of them answered the same question. ### For newcomers and experts Start with the leaderboard tab for the overall order, then open the data viewer to read a few questions and their gold answers. Each system's files share a short key, written in lowercase with underscores. Keys already registered include bm25, langchain, ragflow, vertexai and weaviate\_verba. The transform script finds systems from the result files and checks that every required file and mapping exists. A new system should go live only when all 500 questions have valid, populated records, and a partial run should not be pushed. The files under final\_display\_data are generated and are not meant to be edited by hand. Raw files go in data/raw\_data → A display name is added to systems.yaml → transform\_raw\_data.py builds the display data → The Gradio app shows the tabs ### Good to know - Scale: The base question set holds 500 questions. - Rule: Do not publish a partial run. Every record needs an answer and filled scores. - License: mit. - Cost to run: none named. - Data it reaches: The evaluation files bundled in the repository. The record names one dataset, EnterpriseRAG-Bench. ## README ````markdown # EnterpriseRAG Bench Leaderboard Ranks retrieval-augmented generation systems on EnterpriseRAG-Bench, a benchmark built on company knowledge questions, with tabs to browse questions and compare systems side by side. For teams choosing a RAG system. Made by onyx-dot-app on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: EnterpriseRAG Bench Leaderboard is a Gradio app that ranks RAG systems on EnterpriseRAG-Bench, a benchmark about a company's internal knowledge. The base set holds 500 questions, and each system is scored on its answers. You can open it online, or build and run your own copy. Who it suits: It suits teams who are comparing retrieval-augmented generation systems for use on internal documents, and people who maintain the benchmark. ```table Industry | Who uses it | How they use it | Example Enterprise software | Platform teams | Compare RAG systems before choosing one | Open the ranked table and read the order AI research | Researchers | Study how systems do on different question types | Open the per-system breakdown Quality assurance | Evaluators | Check individual answers against gold answers | Step through one system's questions Benchmark maintenance | Maintainers | Add a new system to the board | Follow the steps for a new submission ``` Skip it if you want to run your own evaluation from scratch, since the app displays results that were scored elsewhere. Where to use it: - A web browser, on the Space's page at Hugging Face - Docker on your own Windows, macOS or Linux computer - Python on your own computer, for a direct local run ```table Use case | Who | What they get back Rank systems | Teams | A table of systems in order of score Inspect one system | Evaluators | Its answers question by question Compare two systems | Researchers | Their answers side by side ``` How to set it up: The online page needs nothing. A local copy needs Docker, or Python with the packages in requirements.txt, and Python with pip to fetch the files. - Docker, or Python with pip - Python packages from requirements.txt, for a direct run - Windows, macOS or Linux 1. Install the Hugging Face hub library. 2. Download the Space's files into a folder named EnterpriseRAG-Bench-Leaderboard. 3. Move into that folder. 4. Build the Docker image. 5. Run the container with port 7860 published, then open that port in your browser. ```sh pip install -U huggingface_hub hf download onyx-dot-app/EnterpriseRAG-Bench-Leaderboard --repo-type space --local-dir EnterpriseRAG-Bench-Leaderboard cd EnterpriseRAG-Bench-Leaderboard docker build -t enterpriserag-bench-leaderboard . docker run -it -p 7860:7860 enterpriserag-bench-leaderboard ``` To skip Docker, install the packages and start the app directly. It serves on port 7860. ```bash pip install -r requirements.txt python app.py ``` How to use it: Open the page and move between the tabs. ```table Tool | What it does Leaderboard tab | The main table of systems, ranked Submission overview tab | A breakdown for each system by question type Data viewer tab | Lets you browse the questions, the gold answers and the facts Submission viewer tab | Shows one system's answers question by question Side-by-side tab | Puts two systems next to each other ``` To add a system, put its answer, result and corrected question files in data/raw_data, add a display name in systems.yaml, then regenerate the display data. ```yaml your_key: "Your System Display Name" ``` ```bash python transform_raw_data.py ``` Example: You open the leaderboard tab to see how the systems rank. You then switch to the side-by-side tab and compare how two of them answered the same question. For newcomers and experts: Start with the leaderboard tab for the overall order, then open the data viewer to read a few questions and their gold answers. Each system's files share a short key, written in lowercase with underscores. Keys already registered include bm25, langchain, ragflow, vertexai and weaviate_verba. The transform script finds systems from the result files and checks that every required file and mapping exists. A new system should go live only when all 500 questions have valid, populated records, and a partial run should not be pushed. The files under final_display_data are generated and are not meant to be edited by hand. ```flow Raw files go in data/raw_data A display name is added to systems.yaml transform_raw_data.py builds the display data The Gradio app shows the tabs ``` Good to know: - Scale: The base question set holds 500 questions. - Rule: Do not publish a partial run. Every record needs an answer and filled scores. - License: mit. - Cost to run: none named. - Data it reaches: The evaluation files bundled in the repository. The record names one dataset, EnterpriseRAG-Bench. ## On Hugging Face, 13 November 2026 - Likes: 40 - Built with: docker - License: mit ## Links - Space: https://huggingface.co/spaces/onyx-dot-app/EnterpriseRAG-Bench-Leaderboard - Files: https://huggingface.co/spaces/onyx-dot-app/EnterpriseRAG-Bench-Leaderboard/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [onyx-dot-app/EnterpriseRAG-Bench-Leaderboard on Hugging Face](https://huggingface.co/spaces/onyx-dot-app/EnterpriseRAG-Bench-Leaderboard) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | EnterpriseRAG Bench Leaderboard (this page) | 40 | docker | mit | 2026-11-03 | | [Open LLM Leaderboard](https://mindket.com/p/f179f58f-4514-4b60-b61f-b86395bec554) | 14,156 | docker | apache-2.0 | 2026-05-27 | | [MTEB Leaderboard](https://mindket.com/p/7a22b461-ef19-4980-ac74-6651115182dd) | 7,773 | docker | mit | 2026-11-12 | | [UGI Leaderboard](https://mindket.com/p/85570e8b-1c6f-4d8a-bede-5aac8be604d5) | 2,199 | docker | apache-2.0 | 2026-10-17 | | [Microduck Sandbox](https://mindket.com/p/3ee8f9e8-61ad-4382-b1f0-74011211142f) | 804 | docker | apache-2.0 | 2026-11-06 | | [Visualize Dataset (v2.0+ latest dataset format)](https://mindket.com/p/a41cd404-e729-4615-9dc9-3d957f6bb38e) | 754 | docker | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-03 - First published: 2026-04-13 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open onyx-dot-app/EnterpriseRAG-Bench-Leaderboard on Hugging Face](https://huggingface.co/spaces/onyx-dot-app/EnterpriseRAG-Bench-Leaderboard) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4e9f2668-1240-4ffd-be70-1e5a4f93e5da) - [Its page on Mindket](https://mindket.com/p/4e9f2668-1240-4ffd-be70-1e5a4f93e5da)